Passive Ballistic Microbunching of Nonultrarelativistic Electron Bunches Using Electromagnetic Wakefields in Dielectric-Lined Waveguides

Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator

We present a general adaptive latent space tuning approach for improving the robustness of machine learning tools with respect to time variation and distribution shift. We demonstrate our approach by developing an encoder-decoder convolutional neural network-based virtual 6D phase space diagnostic of charged particle beams in the HiRES ultrafast electron diffraction (UED) compact particle accelerator with uncertainty quantification. Our method utilizes model-independent adaptive feedback to tune a low-dimensional 2D latent space representation of ∼1 million dimensional objects which are the 15 unique 2D projections (x,y),...,(z,p_{z}) of the 6D phase space (x,y,z,p_{x},p_{y},p_{z}) of the charged particle beams. We demonstrate our method with numerical studies of short electron bunches utilizing experimentally measured UED input beam distributions.

Enhanced Versatility of Table-Top X-Rays from Van der Waals Structures

Van der Waals (vdW) materials have attracted much interest for their myriad unique electronic, mechanical, and thermal properties. In particular, they are promising candidates for monochromatic, table-top X-ray sources. This work reveals that the versatility of the table-top vdW X-ray source goes beyond what has been demonstrated so far. By introducing a tilt angle between the vdW structure and the incident electron beam, it is theoretically and experimentally shown that the accessible photon energy range is more than doubled. This allows for greater versatility in real-time tuning of the vdW X-ray source. Furthermore, this work shows that the accessible photon energy range is maximized by simultaneously controlling both the electron energy and the vdW structure tilt. These results will pave the way for highly tunable, compact X-ray sources, with potential applications including hyperspectral X-ray fluoroscopy and X-ray quantum optics.

An adaptive approach to machine learning for compact particle accelerators

Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

Intrinsically reducing divergence angle of Cherenkov radiation from dielectric capillary

Narrow-band terahertz (THz) Cherenkov radiation can be excited as a relativistic electron bunch passes through the dielectric capillary with sub-millimeter radius. However, due to the diffraction effect, the radiation will enter free space with a large divergence angle, which makes it difficult to collect the radiation energy efficiently. In this Letter, to deal with this challenge, we propose to add a new dielectric layer, which satisfies a special relationship with the electron velocity, between the metal coating and original dielectric layer in the capillary. According to numerical simulation and theoretical analysis results, the divergence angle of radiation is significantly suppressed, and the peak power density is also enhanced by over two orders. As a result, the transmission efficiency from the radiation source to the optical system can be increased to over 90%. We expect that this method will provide a new way to generate THz Cherenkov radiation.

Net Acceleration and Direct Measurement of Attosecond Electron Pulses in a Silicon Dielectric Laser Accelerator

Net acceleration of attosecond-scale electron pulses is critical to the development of on-chip accelerators. We demonstrate a silicon-based laser-driven two-stage accelerator as an injector stage prototype for a Dielectric Laser Accelerator (DLA). The first stage converts a 57-keV (500±100)-fs (FWHM) electron pulse into a pulse train of 700±200 as (FWHM) microbunches. The second stage harnesses the tunability of dual-drive DLA to perform both a net acceleration and a streaking measurement. In the acceleration mode, the second stage increases the net energy of the electron pulse by 200 eV over 12.25  μm. In the deflection mode, the microbunch temporal profile is analyzed by a direct streaking measurement with 200 as resolution. This work provides a demonstration of a novel, on-chip method to access the attosecond regime, opening new paths towards attosecond science using DLA.